An empirical study of three machine learning methods for spam filtering

✍ Scribed by Chih-Chin Lai

• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 153 KB
• Volume: 20
• Category: Article
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2006.05.016

✦ Synopsis

The increasing volumes of unsolicited bulk e-mail (also known as spam) are bringing more annoyance for most Internet users. Using a classifier based on a specific machine-learning technique to automatically filter out spam e-mail has drawn many researchers' attention. This paper is a comparative study the performance of three commonly used machine learning methods in spam filtering. On the other hand, we try to integrate two spam filtering methods to obtain better performance. A set of systematic experiments has been conducted with these methods which are applied to different parts of an e-mail. Experiments show that using the header only can achieve satisfactory performance, and the idea of integrating disparate methods is a promising way to fight spam.